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Practical approaches to causal relat...

Li, Jiuyong.

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Practical approaches to causal relationship exploration

Record Type:	Electronic resources : Monograph/item
Title/Author:	Practical approaches to causal relationship exploration/ by Jiuyong Li, Lin Liu, Thuc Duy Le.
Author:	Li, Jiuyong.
other author:	Liu, Lin.
Published:	Cham :Springer International Publishing : : 2015.,
Description:	x, 80 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Local causal discovery with a simple PC algorithm -- A local causal discovery algorithm for high dimensional data -- Causal rule discovery with partial association test -- Causal rule discovery with cohort studies -- Experimental comparison and discussions.
Contained By:	Springer eBooks
Subject:	Data mining. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-14433-7
ISBN:	9783319144337 (electronic bk.)

Practical approaches to causal relationship exploration
Li, Jiuyong.

Practical approaches to causal relationship exploration[electronic resource] /by Jiuyong Li, Lin Liu, Thuc Duy Le. - Cham :Springer International Publishing :2015. - x, 80 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering,2191-8112. - SpringerBriefs in electrical and computer engineering..

Introduction -- Local causal discovery with a simple PC algorithm -- A local causal discovery algorithm for high dimensional data -- Causal rule discovery with partial association test -- Causal rule discovery with cohort studies -- Experimental comparison and discussions.

This brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods apply conditional independence tests for causal discovery. The last two methods employ association rule mining for efficient causal hypothesis generation, and a partial association test and retrospective cohort study for validating the hypotheses. All four methods are innovative and effective in identifying potential causal relationships around a given target, and each has its own strength and weakness. For each method, a software tool is provided along with examples demonstrating its use. Practical Approaches to Causal Relationship Exploration is designed for researchers and practitioners working in the areas of artificial intelligence, machine learning, data mining, and biomedical research. The material also benefits advanced students interested in causal relationship discovery.

ISBN: 9783319144337 (electronic bk.)

Standard No.: 10.1007/978-3-319-14433-7doiSubjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Practical approaches to causal relationship exploration
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100 1 $a Li, Jiuyong. $3 2139159
245 1 0 $a Practical approaches to causal relationship exploration $h [electronic resource] / $c by Jiuyong Li, Lin Liu, Thuc Duy Le.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a x, 80 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in electrical and computer engineering, $x 2191-8112
505 0 $a Introduction -- Local causal discovery with a simple PC algorithm -- A local causal discovery algorithm for high dimensional data -- Causal rule discovery with partial association test -- Causal rule discovery with cohort studies -- Experimental comparison and discussions.
520 $a This brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods apply conditional independence tests for causal discovery. The last two methods employ association rule mining for efficient causal hypothesis generation, and a partial association test and retrospective cohort study for validating the hypotheses. All four methods are innovative and effective in identifying potential causal relationships around a given target, and each has its own strength and weakness. For each method, a software tool is provided along with examples demonstrating its use. Practical Approaches to Causal Relationship Exploration is designed for researchers and practitioners working in the areas of artificial intelligence, machine learning, data mining, and biomedical research. The material also benefits advanced students interested in causal relationship discovery.
650 0 $a Data mining. $3 562972
650 0 $a Causation. $3 564655
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
700 1 $a Liu, Lin. $3 1939069
700 1 $a Le, Thuc Duy. $3 2139160
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in electrical and computer engineering. $3 1565565
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-14433-7
950 $a Computer Science (Springer-11645)